In recent years, GNSS has been included in a rapidly increasing number of applications in various sectors, including those regarded to be critical as they concern safety and financial transactions. A major threat to the widespread adoption and use of GNSS is its vulnerability to signal interference and jamming, which can severely degrade the GNSS service and impact performance. Effects range from a loss of accuracy to complete denial of GNSS services. The threat of unintentional RFI has been recognized for some time and becomes greater with an increasingly crowded RF spectrum and ever more devices using these frequencies. Intentional jamming is an increasing problem due to the low cost and wide availability of civilian jammers such as so called personal privacy protection device (PPD).
There is a desire and/or need for the provision of an effective means of detecting for the presence of a GNSS jamming signal, whether resulting from intentional or unintentional signals. Such jamming signals will typically be RF signals (RF Interference or “RFI”). It would be desirable for any such detector of a GNSS jamming signal to be capable of classifying the GNSS jamming signal and determining its likely location. Knowing the type and location of the interference source may lead to more suitable and effective mitigation actions.
There are different GNSS RFI detectors in the public domain which suffer from disadvantages that render such detectors as inadequate for the purposes of GNSS jamming signal detection and/or classification and/or which can otherwise be improved. One such GNSS RFI receiver (as discussed in the GPS World paper entitled “Lone Sentinel: Single-Receiver Sensitivity to RF Interference”—available from http://gpsworld.com/gnss-systemsignal-processinglone-sentinel-11844) relies on the measurements from commercial off the shelf (COTS) GPS/GNSS receivers, in that the RFI detection is based on the receiver estimated signal-to-noise-ratio (SNR) [or equivalently the carrier-to-noise-density (CN0)]. A first drawback of this type of technique is caused by the limited transparency and the variation of the COTS receivers' proprietary designs. In order to cope with this variation, the jamming signal detection algorithms have to loosen the detection criteria to tolerate this variation. A second drawback of this type of technique is that the SNR or CN0 is usually estimated after the GNSS correlation stage and is usually filtered in order to reduce the noise of the estimate. This process reduces the sensitivity of the detection and introduces a delay that is proportional to the filter time constant. Such a receiver is also unable to provide sufficient information or data for detailed classification of the jamming signal to be effected.
Measurements such as tracking loop loss of lock indicator, code and carrier coherence, and automatic gain control (AGC) measurements have each also been proposed as alternative means for enabling GNSS jamming signal detection. But these techniques encounter similar drawbacks as the ones relying on the SNR or CN0.
Another drawback with the use of certain metrics for detecting RFI is that some such metrics may vary due to factors which are not related to interference. GNSS signal strengths vary depending on the local operating environments, with satellite signals being reflected, diffracted and completely obscured. If a system only takes into account CN0, or the numbers of satellites tracked, for example, it is not possible to determine if the cause of any degradation is RFI.
Another type of system proposed (see for example, the paper by Wende, J.; Kurzhals, C.; Houdek, M.; Samson, J., “An interference monitoring system for GNSS reference stations,” Antenna Technology and Applied Electromagnetics (ANTEM), 2012 15th International Symposium on, vol., no., pp. 1, 5, 25-28 Jun. 2012) for detecting GNSS jamming signal is a COTS real-time spectrum analyser. Such a spectrum analyser could be used to monitor and characterize the RFI by performing continuous spectrum analyses. Benefiting from a powerful spectrum analysing capability, such a system may offer better sensitivity compared to the techniques mentioned above. However, although more powerful, such a real-time spectrum analyser is very expensive and is more sophisticated in many ways that are of no use for detecting GNSS jamming signals. Indeed, most of the functionalities of such a COTS spectrum analyzer are not needed for GNSS jamming signal detection. The integration of such a COTS spectrum analyzer into an easy-to-use and practical GNSS jamming signal detection system is not a trivial task. Such a COTS spectrum analyzer may thus be limited in its application to one-off surveys of a specific site where a period of data can be captured and analyzed later manually. Such a COTS spectrum analyzer is not a cost-effective or practical solution for the majority of desired applications.
Various academic papers have been presented on the subject of GNSS RFI detection and characterisation but such papers fail to provide sufficient details to enable a person skilled in the art to manufacture a fully operational GNSS RFI detector without further details and/or without expending further inventive activity.
The present invention seeks to mitigate one or more of the above-mentioned problems/disadvantages. Alternatively or additionally, the present invention seeks to provide an improved method of detecting a GNSS jamming signal. Alternatively or additionally, the present invention seeks to provide an improved method of classifying a GNSS jamming signal. Alternatively or additionally, the present invention seeks to provide an improved method of geo-locating a GNSS jamming signal. Alternatively or additionally, the present invention seeks to provide an improved GNSS jamming signal detector.